{"total":293,"rows":[{"id":"2502.09692","title":"AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers","created":"2025-02-13","cited_by_count":53,"area":null},{"id":"2406.02176","title":"AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields","created":"2024-06-04","cited_by_count":39,"area":null},{"id":"2405.17527","title":"Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers","created":"2024-05-27","cited_by_count":35,"area":null},{"id":"2403.08055","title":"DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction","created":"2024-03-12","cited_by_count":33,"area":null},{"id":"2505.24717","title":"PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations","created":"2025-05-30","cited_by_count":32,"area":null},{"id":"2408.12171","title":"Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey","created":"2024-08-22","cited_by_count":28,"area":null},{"id":"2402.12365","title":"Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators","created":"2024-02-19","cited_by_count":27,"area":null},{"id":"2512.20399","title":"GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer","created":"2025-12-23","cited_by_count":23,"area":null},{"id":"2502.17019","title":"Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems","created":"2025-02-24","cited_by_count":22,"area":null},{"id":"2602.04940","title":"Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries","created":"2026-02-04","cited_by_count":20,"area":null},{"id":"2408.01600","title":"Physics-Informed Geometry-Aware Neural Operator","created":"2024-08-02","cited_by_count":19,"area":null},{"id":"2406.06660","title":"Space-Time Continuous PDE Forecasting using Equivariant Neural Fields","created":"2024-06-10","cited_by_count":18,"area":null},{"id":"2501.18972","title":"BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics","created":"2025-01-31","cited_by_count":18,"area":null},{"id":"2411.15178","title":"Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries","created":"2024-11-18","cited_by_count":16,"area":null},{"id":"2507.20065","title":"Geometric Operator Learning with Optimal Transport","created":"2025-07-26","cited_by_count":16,"area":null},{"id":"2601.01829","title":"RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data","created":"2026-01-05","cited_by_count":16,"area":null},{"id":"2503.17400","title":"TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks","created":"2025-03-19","cited_by_count":14,"area":null},{"id":"2411.00040","title":"P$^2$C$^2$Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics","created":"2024-10-29","cited_by_count":13,"area":null},{"id":"2511.06294","title":"Transolver is a Linear Transformer: Revisiting Physics-Attention through the Lens of Linear Attention","created":"2025-11-09","cited_by_count":13,"area":null},{"id":"2503.03178","title":"Active operator learning with predictive uncertainty quantification for partial differential equations","created":"2025-03-05","cited_by_count":12,"area":null},{"id":"2405.17211","title":"Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows","created":"2024-05-27","cited_by_count":11,"area":null},{"id":"2407.00809","title":"Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning","created":"2024-06-30","cited_by_count":11,"area":null},{"id":"2505.12044","title":"FlashBias: Fast Computation of Attention with Bias","created":"2025-05-17","cited_by_count":11,"area":null},{"id":"2505.18190","title":"PhySense: Sensor Placement Optimization for Accurate Physics Sensing","created":"2025-05-19","cited_by_count":11,"area":null},{"id":"2506.16656","title":"Mesh-Informed Neural Operator : A Transformer Generative Approach","created":"2025-06-20","cited_by_count":11,"area":null},{"id":"2410.11382","title":"Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space","created":"2024-10-15","cited_by_count":10,"area":null},{"id":"2411.09678","title":"NeuralDEM -- Real-time Simulation of Industrial Particulate Flows","created":"2024-11-14","cited_by_count":10,"area":null},{"id":"2505.14704","title":"Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations","created":"2025-05-14","cited_by_count":10,"area":null},{"id":"2512.07847","title":"CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics","created":"2025-11-25","cited_by_count":10,"area":null},{"id":"2601.23177","title":"MeshGraphNet-Transformer: Scalable Mesh-based Learned Simulation for Solid Mechanics","created":"2026-01-30","cited_by_count":10,"area":null},{"id":"2602.20399","title":"GeoPT: Scaling Physics Simulation via Lifted Geometric Pre-Training","created":"2026-02-23","cited_by_count":10,"area":null},{"id":"2505.19038","title":"Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias","created":"2025-05-25","cited_by_count":9,"area":null},{"id":"2412.13074","title":"Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates","created":"2024-12-17","cited_by_count":8,"area":null},{"id":"2502.08574","title":"TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion","created":"2025-02-12","cited_by_count":8,"area":null},{"id":"2503.10257","title":"AMR-Transformer: Enabling Efficient Long-range Interaction for Complex Neural Fluid Simulation","created":"2025-03-13","cited_by_count":8,"area":null},{"id":"2507.02227","title":"PhysicsCorrect: A Training-Free Approach for Stable Neural PDE Simulations","created":"2025-07-03","cited_by_count":8,"area":null},{"id":"2602.02788","title":"Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs","created":"2026-02-02","cited_by_count":8,"area":null},{"id":"2410.23889","title":"GEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning","created":"2024-10-31","cited_by_count":7,"area":null},{"id":"2506.12007","title":"SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts","created":"2025-06-13","cited_by_count":7,"area":null},{"id":"2510.07314","title":"GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations","created":"2025-10-08","cited_by_count":7,"area":null},{"id":"2510.16857","title":"DrivAerStar: An Industrial-Grade CFD Dataset for Vehicle Aerodynamic Optimization","created":"2025-10-19","cited_by_count":7,"area":null},{"id":"2406.04822","title":"M2NO: An Efficient Multi-Resolution Operator Framework for Dynamic Multi-Scale PDE Solvers","created":"2024-06-07","cited_by_count":6,"area":null},{"id":"2504.00510","title":"Operator Learning with Domain Decomposition for Geometry Generalization in PDE Solving","created":"2025-04-01","cited_by_count":6,"area":null},{"id":"2504.08277","title":"Enabling Automatic Differentiation with Mollified Graph Neural Operators","created":"2025-04-11","cited_by_count":6,"area":null},{"id":"2504.18591","title":"Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations","created":"2025-04-24","cited_by_count":6,"area":null},{"id":"2508.00101","title":"Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method","created":"2025-07-31","cited_by_count":6,"area":null},{"id":"2508.12594","title":"FLARE: Fast Low-rank Attention Routing Engine","created":"2025-08-18","cited_by_count":6,"area":null},{"id":"2510.24173","title":"EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale","created":"2025-10-28","cited_by_count":6,"area":null},{"id":"2511.01924","title":"Neural Green's Functions","created":"2025-11-02","cited_by_count":6,"area":null},{"id":"2511.18777","title":"SAOT: An Enhanced Locality-Aware Spectral Transformer for Solving PDEs","created":"2025-11-24","cited_by_count":6,"area":null},{"id":"2511.21474","title":"Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes","created":"2025-11-26","cited_by_count":6,"area":null},{"id":"2512.00422","title":"PhysGen: Physically Grounded 3D Shape Generation for Industrial Design","created":"2025-11-29","cited_by_count":6,"area":null},{"id":"2409.19221","title":"Cauchy activation function and XNet","created":"2024-09-28","cited_by_count":5,"area":null},{"id":"2410.20100","title":"Latent Neural Operator Pretraining for Solving Time-Dependent PDEs","created":"2024-10-26","cited_by_count":5,"area":null},{"id":"2505.01736","title":"PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems","created":"2025-05-03","cited_by_count":5,"area":null},{"id":"2505.20349","title":"FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation","created":"2025-05-25","cited_by_count":5,"area":null},{"id":"2507.09264","title":"Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators","created":"2025-07-12","cited_by_count":5,"area":null},{"id":"2508.18051","title":"Training Transformers for Mesh-Based Simulations","created":"2025-08-25","cited_by_count":5,"area":null},{"id":"2510.15808","title":"AB-UPT for Automotive and Aerospace Applications","created":"2025-10-17","cited_by_count":5,"area":null},{"id":"2511.08811","title":"A Neural-Operator Preconditioned Newton Method for Accelerated Nonlinear Solvers","created":"2025-11-11","cited_by_count":5,"area":null},{"id":"2602.03582","title":"Optimization and Generation in Aerodynamics Inverse Design","created":"2026-02-03","cited_by_count":5,"area":null},{"id":"2602.10150","title":"PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation","created":"2026-02-09","cited_by_count":5,"area":null},{"id":"2604.16429","title":"(Sparse) Attention to the Details: Preserving Spectral Fidelity in ML-based Weather Forecasting Models","created":"2026-04-06","cited_by_count":5,"area":null},{"id":"2505.22573","title":"FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2601.18707","title":"SMART: Scalable Mesh-free Aerodynamic Simulations from Raw Geometries using a Transformer-based Surrogate Model","created":"2026-01-26","cited_by_count":4,"area":null},{"id":"2603.16849","title":"GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators","created":"2026-03-17","cited_by_count":4,"area":null},{"id":"2604.03582","title":"Simple yet Effective: Low-Rank Spatial Attention for Neural Operators","created":"2026-04-04","cited_by_count":4,"area":null},{"id":"2605.05586","title":"AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling","created":"2026-05-07","cited_by_count":4,"area":null},{"id":"2605.27758","title":"High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention","created":"2026-05-26","cited_by_count":4,"area":null},{"id":"2606.09806","title":"Topological Neural Operators","created":"2026-06-08","cited_by_count":4,"area":null},{"id":"2408.06101","title":"Generalization capabilities of MeshGraphNets to unseen geometries for fluid dynamics","created":"2024-08-12","cited_by_count":3,"area":null},{"id":"2412.16644","title":"An explainable operator approximation framework under the guideline of Green's function","created":"2024-12-21","cited_by_count":3,"area":null},{"id":"2506.06001","title":"LaDEEP: A Deep Learning-based Surrogate Model for Large Deformation of Elastic-Plastic Solids","created":"2025-06-06","cited_by_count":3,"area":null},{"id":"2506.07969","title":"A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling","created":"2025-06-09","cited_by_count":3,"area":null},{"id":"2512.01738","title":"MSPT: Efficient Large-Scale Physical Modeling via Parallelized Multi-Scale Attention","created":"2025-12-01","cited_by_count":3,"area":null},{"id":"2512.14397","title":"SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design","created":"2025-12-16","cited_by_count":3,"area":null},{"id":"2512.14596","title":"Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs","created":"2025-12-16","cited_by_count":3,"area":null},{"id":"2602.04923","title":"Imposing Boundary Conditions on Neural Operators via Learned Function Extensions","created":"2026-02-04","cited_by_count":3,"area":null},{"id":"2602.05052","title":"Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm","created":"2026-02-04","cited_by_count":3,"area":null},{"id":"2602.11626","title":"ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning","created":"2026-02-12","cited_by_count":3,"area":null},{"id":"2602.15472","title":"Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows","created":"2026-02-17","cited_by_count":3,"area":null},{"id":"2603.12635","title":"Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems","created":"2026-03-13","cited_by_count":3,"area":null},{"id":"2605.14546","title":"Discovering Physical Directions in Weight Space: Composing Neural PDE Experts","created":"2026-05-14","cited_by_count":3,"area":null},{"id":"2605.15305","title":"WorldParticle: Unified World Simulation of Lagrangian Particle Dynamics via Transformer","created":"2026-05-14","cited_by_count":3,"area":null},{"id":"2407.03925","title":"Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction","created":"2024-07-04","cited_by_count":2,"area":null},{"id":"2505.14039","title":"Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operators","created":"2025-05-20","cited_by_count":2,"area":null},{"id":"2505.14717","title":"AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD","created":"2025-05-19","cited_by_count":2,"area":null},{"id":"2506.13906","title":"GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations","created":"2025-06-16","cited_by_count":2,"area":null},{"id":"2506.17230","title":"MMET: A Multi-Input and Multi-Scale Transformer for Efficient PDEs Solving","created":"2025-05-24","cited_by_count":2,"area":null},{"id":"2508.04463","title":"GFocal: A Global-Focal Neural Operator for Solving PDEs on Arbitrary Geometries","created":"2025-08-06","cited_by_count":2,"area":null},{"id":"2509.09611","title":"ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance","created":"2025-09-11","cited_by_count":2,"area":null},{"id":"2509.24332","title":"Towards Generalizable PDE Dynamics Forecasting via Physics-Guided Invariant Learning","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2509.26186","title":"PDE Solvers Should Be Local: Fast, Stable Rollouts with Learned Local Stencils","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2510.16004","title":"PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction","created":"2025-10-14","cited_by_count":2,"area":null},{"id":"2512.04974","title":"Efficient Generative Transformer Operators For Million-Point PDEs","created":"2025-12-04","cited_by_count":2,"area":null},{"id":"2512.16409","title":"Geometric Laplace Neural Operator","created":"2025-12-18","cited_by_count":2,"area":null},{"id":"2602.21551","title":"From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators","created":"2026-02-25","cited_by_count":2,"area":null},{"id":"2603.01193","title":"Operator Learning Using Weak Supervision from Walk-on-Spheres","created":"2026-03-01","cited_by_count":2,"area":null},{"id":"2603.01762","title":"DGNet: Discrete Green Networks for Data-Efficient Learning of Spatiotemporal PDEs","created":"2026-03-02","cited_by_count":2,"area":null},{"id":"2603.04430","title":"Flowers: A Warp Drive for Neural PDE Solvers","created":"2026-02-17","cited_by_count":2,"area":null}],"next_cursor":"100"}